103 citations · 261 across the 10 of their papers we have counts for
10 papers
Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection
Zizhang Wu, Yunzhe Wu, Jian Pu +2
Monocular 3D object detection is a low-cost but challenging task, as it requires generating accurate 3D localization solely from a single image input. Recent developed depth-assist…
Towards Robust Part-aware Instance Segmentation for Industrial Bin Picking
Yidan Feng, Biqi Yang, Xianzhi Li +7
Industrial bin picking is a challenging task that requires accurate and robust segmentation of individual object instances. Particularly, industrial objects can have irregular shap…
PointAugment: an Auto-Augmentation Framework for Point Cloud Classification
Ruihui Li, Xianzhi Li, Pheng-Ann Heng +1
We present PointAugment, a new auto-augmentation framework that automatically optimizes and augments point cloud samples to enrich the data diversity when we train a classification…
Non-Local Part-Aware Point Cloud Denoising
Chao Huang, Ruihui Li, Xianzhi Li +1
This paper presents a novel non-local part-aware deep neural network to denoise point clouds by exploring the inherent non-local self-similarity in 3D objects and scenes. Different…
A Rotation-Invariant Framework for Deep Point Cloud Analysis
Xianzhi Li, Ruihui Li, Guangyong Chen +3
Recently, many deep neural networks were designed to process 3D point clouds, but a common drawback is that rotation invariance is not ensured, leading to poor generalization to ar…
Deep Floor Plan Recognition Using a Multi-Task Network with Room-Boundary-Guided Attention
Zhiliang Zeng, Xianzhi Li, Ying Kin Yu +1
This paper presents a new approach to recognize elements in floor plan layouts. Besides walls and rooms, we aim to recognize diverse floor plan elements, such as doors, windows and…
PU-GAN: a Point Cloud Upsampling Adversarial Network
Ruihui Li, Xianzhi Li, Chi-Wing Fu +2
Point clouds acquired from range scans are often sparse, noisy, and non-uniform. This paper presents a new point cloud upsampling network called PU-GAN, which is formulated based o…
Unsupervised Detection of Distinctive Regions on 3D Shapes
Xianzhi Li, Lequan Yu, Chi-Wing Fu +2
This paper presents a novel approach to learn and detect distinctive regions on 3D shapes. Unlike previous works, which require labeled data, our method is unsupervised. We conduct…
EC-Net: an Edge-aware Point set Consolidation Network
Lequan Yu, Xianzhi Li, Chi-Wing Fu +2
Point clouds obtained from 3D scans are typically sparse, irregular, and noisy, and required to be consolidated. In this paper, we present the first deep learning based edge-aware…
PU-Net: Point Cloud Upsampling Network
Lequan Yu, Xianzhi Li, Chi-Wing Fu +2
Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data. In this paper, we present a data-driven point cloud ups…